Frailty measurement in research and clinical practice: An updated review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Frailty is a highly prevalent geriatric condition, affecting between 12-24% of older adults globally. It remains a major cause of morbidity and mortality in older adults. Incorporating frailty measurement into clinical decision making can guide optimal patient care. This updated review presents an outline of current frailty definitions and measurement approaches in both research and clinical practice, including: Fried's frailty phenotype; Rockwood and Mitnitski's Frailty Index (FI) of cumulative deficits; Clinical Frailty Scale (CFS); Fatigue, Resistance, Ambulation, Illness and Loss of weight (FRAIL) scale; Edmonton Frail Scale (EFS); electronic Frailty Index (eFI); Hospital Frailty Risk Score (HFRS); Study of Osteoporotic Fractures (SOF) Index; Tilburg Frailty Indicator (TFI); Groningen Frailty Indictor (GFI); Multidimensional Prognostic Index (MPI); the Kihon Checklist (KCL); Geriatric 8 (G8) for oncology; the Essential Frailty Toolset (EFT) for cardiology; plus gait speed and grip strength. The main strengths and limitations of existing frailty measurements are summarised, including how well these measurements operationalise frailty in terms of their accuracy in identifying frailty, their basis on biological causative theory, and their ability to reliably predict patient outcomes and response to potential therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it